Detecting and extracting warm objects from thermal images is a difficult process. The process is prone to false results. Objects that should be detected may be classified as part of the background image, and background pixels may be classified as part of the object of interest.
Conventionally, warm or hot objects may be segmented from a scene by setting a threshold temperature value. If the temperature of the object is above the set threshold, the object may be separated from the remainder of the scene. The threshold may be set to a single value. Values above the threshold may be set to a value of 1 and values below the threshold may be set to a value of 0. In this manner, a warm or hot object may be segmented from the scene.
There are various methods of performing segmentation of a scene. These include, for example, clustering methods, histogram methods, gradient methods, and edge detection methods. Clustering methods attempt to partition an image into predefined number of clusters or classes. Frequently, this approach is iterative and requires calculations of distances between clusters and distances to the center of each cluster. Histogram methods may also be used for segmenting a scene. Histograms depicting distributions of pixel values may be computed and a threshold may be set based on results of the image histograms. Sometimes, a smoothed histogram may be examined for peaks and valleys and a decision may be made based on expectation of maximizing a certain distribution of pixel values.
Clustering or histogram methods are typically low power consumers, but result in undesired increases in false target classifications. This is due to computing a minimum value or maximum value, while failing to account for benign scene contents. In addition, these methods are likely to fail, because they fail to account for scene changes occurring due to camera movements or complete changes in contents of the scenes.
Additional segmentation methods may also include Fourier analysis, pattern classification, or template matching. These approaches consume a large amount of computational power and are generally not suitable for use by hand-held thermal cameras, or devices that use FPGAs operating on battery power.
As will be explained, the present invention uses multiple methods that together provide a reliable global threshold for segmenting a target of interest from a background scene in a set of video frames. The present invention advantageously consumes little power and achieves a low false alarm rate. The present invention may be reliably used in an environment, in which a user rapidly moves the camera and the scene rapidly changes between frames.